A deep learning based stereo matching model for autonomous vehicle

Autonomous vehicle is one the prominent area of research in computer vision. In today’s AI world, the concept of autonomous vehicles has become popular largely to avoid accidents due to negligence of driver. Perceiving the depth of the surrounding region accurately is a challenging task in autonomous vehicles. Sensors like light detection and ranging can be used for depth estimation but these sensors are expensive. Hence stereo matching is an alternate solution to estimate the depth. The main difficulties observed in stereo matching is to minimize mismatches in the ill-posed regions, like occluded, texture less and discontinuous regions. This paper presents an efficient deep stereo matching technique for estimating disparity map from stereo images in ill-posed regions. The images from Middlebury stereo data set are used to assess the efficacy of the model proposed. The experimental outcome dipicts that the proposed model generates reliable results in the occluded, texture less and discontinuous regions as compared to the existing techniques.